Package: HCPclust 0.1.2

Menghan Yi

HCPclust: Hierarchical Conformal Prediction for Clustered Data with Missing Responses

Implements hierarchical conformal prediction for clustered data with missing responses. The method uses repeated cluster-level splitting and within-cluster subsampling to accommodate dependence, and inverse-probability weighting to correct distribution shift induced by missingness. Conditional densities are estimated by inverting fitted conditional quantiles (linear quantile regression or quantile regression forests), and p-values are aggregated across resampling and splitting steps using the Cauchy combination test.

Authors:Menghan Yi [aut, cre], Judy Wang [aut]

HCPclust_0.1.2.tar.gz
HCPclust_0.1.2.zip(r-4.7-any)HCPclust_0.1.2.zip(r-4.6-any)HCPclust_0.1.2.zip(r-4.5-any)
HCPclust_0.1.2.tgz(r-4.6-any)HCPclust_0.1.2.tgz(r-4.5-any)
HCPclust_0.1.2.tar.gz(r-4.7-any)HCPclust_0.1.2.tar.gz(r-4.6-any)
HCPclust_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
HCPclust/json (API)

# Install 'HCPclust' in R:
install.packages('HCPclust', repos = c('https://judywangstat.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/judywangstat/hcp/issues

On CRAN:

Conda:

3.00 score 1 scripts 191 downloads 6 exports 19 dependencies

Last updated from:d14edc3a03. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK187
source / vignettesOK183
linux-releaseOK162
macos-releaseOK196
macos-oldrelOK233
windows-develOK85
windows-releaseOK78
windows-oldrelOK89
wasm-releaseOK91

Exports:fit_cond_density_quantilefit_missingness_propensitygenerate_clustered_marhcp_conformal_regionhcp_predict_targetsplot_hcp_intervals

Dependencies:data.tableDiceKriginggrfjsonlitelatticelmtestMASSMatrixMatrixModelsquantregquantregForestrandomForestRcppRcppEigensandwichSparseMsurvivalxgboostzoo